Machine learning for fraud detection in financial transactions

Misrepresentation identification in monetary exchanges, especially in Visa utilization, is a basic use of AI that plans to distinguish deceitful exercises from huge measures of exchange information. This paper investigates different AI strategies and techniques utilized in distinguishing charge card misrepresentation. Key methodologies include supervised learning models such as logistic regression, decision trees, random forests, gradient boosting, and neural networks, which are trained on labeled datasets to identify fraudulent and legitimate transactions. Unaided learning strategies, like grouping and peculiarity discovery, are utilized to recognize examples and abnormalities in unlabeled information. Feature engineering plays a crucial role in extracting meaningful attributes from raw transaction data to enhance model performance. Model evaluation is conducted using metrics such as precision, recall, F1-score, and ROC-AUC, with particular attention to the class imbalance problem common in fraud detection datasets. Challenges addressed include evolving fraud tactics, the need for continuous model updates, and balancing detection accuracy with customer satisfaction. The integration of these AI techniques into a robust detection pipeline is presented, offering a comprehensive solution for effective and efficient credit card fraud detection.